As the value and use of information continues to increase, individuals and businesses seek additional ways to process and manage information. Particularly, with tremendous growth in business information, in the magnitude of terabytes, for example, businesses seek adaptive and event-driven information technology to process (e.g., integrate, manage, analyze) the widely distributed data sources for domain dependent applications.
With current information technology, product documentation or data related to goods and services, such as repair documentation or technical documentation, may be static and/or domain dependent with long procedures and transcription errors. Oftentimes, current product documentation may not get updated until the next version of documentation is released. Also, current information technology may not present the most proper or efficient connection among subsystems or processes which derive or process the data to become incorporated into product documentation. In an industry such as automotive services, for example, subsystems or processes such as warranty, logistics, field service, and technical documentation, may not be properly connected to knowledge/data sources such as domain semantics, technical knowledge, natural text verbatim, web data, sensor data, and parts data. Alternatively, the aforementioned subsystems/processes may not be properly connected to objectives such as quality improvement, cost reduction, early issue identification, or the like.
Thus, a need exists for an improved knowledge-driven adaptive service systems, methods, and media to process data among domain dependent applications to improve the methodology for the creation and/or updating of product documentation and knowledge base activities. Such systems, methods, and media may efficiently provide information processing among various segments of an automotive service chain such as product documentation, field service, warranty analysis, and logistics to properly align them with their respective data resources and objectives.